Obstacle detection and notification for motorcycles

ABSTRACT

An obstacle detection and notification system for a motorcycle. The system includes a forward looking camera and a backward looking camera mountable to the motorcycle and a processor in operable communication with the forward looking camera and the backward looking camera. The processor executes program instructions to execute processes including: receiving video from the of the forward looking camera and the backward looking camera, performing a computer vision and machine learning based object detection and tracking process to detect, classify and track obstacles in the video and to output detected object data, defining a blind spot region around one or more other vehicles using the detected object data, determining whether the motorcycle is located in the blind spot region, and outputting audible, tactile or visual feedback, via an output system, to a rider of the motorcycle when the motorcycle is determined to be located in the blind spot region.

INTRODUCTION

The technical field generally relates to obstacle detection andnotification for motorcycles, and more particularly relates to use ofcomputer vision and machine learning to provide feedback of potentialobstacles to a rider.

Automotive Advanced Driver Assistance Systems (also known as “ADAS”)have become, in recent years, a standard in the car industry, inter aliadue to the fact that safety is a main concern for car manufacturers. Aprimary concern of motorcycle riders is collisions with obstacles of anykind. There are some obstacles and situations that are of particularconcern for motorcycle riders that would desirably be addressed by asuitable obstacle detection and notification system.

The motorcycle industry has not, generally, implemented ADAS features,which may be because of the relative cost of ADAS and a motorcycle andalso because there are various difficulties that are specific to themotorcycle's environment. For example, motorcycles have very limitedspace to place ADAS. Providing alerts to motorcycle riders is also achallenge, as the riders wear a helmet, and operate in a noisyenvironment that is affected by wind, engine noise, etc. Furthermore,the viewing angle of a motorcycle rider wearing a helmet is limited, andplacing visual indicators (such as a display for providing visualindications) on the motorcycle itself is challenging in terms of itspositioning on the motorcycle at a location that is visible to the riderwhen riding the motorcycle. Still further, motorcycles behavedifferently than cars, their angles (e.g. lean angle) relative to theroad shift much quicker and more dramatically than car angles withrespect to the road, especially when the motorcycle leans, acceleratesand brakes.

Accordingly, it is desirable to provide systems and methods for obstacledetection and notification for a motorcycle that are low in complexityand cost to implement on a motorcycle and that are able to provideenhanced situational awareness for a motorcycle rider to supportavoiding collisions and accidents. Furthermore, other desirable featuresand characteristics of the present invention will become apparent fromthe subsequent detailed description and the appended claims, taken inconjunction with the accompanying drawings and the foregoing technicalfield and background.

SUMMARY

In one aspect, an obstacle detection and notification system for amotorcycle is disclosed. The system includes a forward looking cameraand a backward looking camera mountable to the motorcycle and aprocessor in operable communication with the forward looking camera andthe backward looking camera. The processor executes program instructionsto execute processes including: receiving video from the of the forwardlooking camera and the backward looking camera, performing a computervision and machine learning based object detection and tracking processto detect, classify and track obstacles in the video and to outputdetected object data, defining a blind spot region around one or moreother vehicles using the detected object data, determining whether themotorcycle is located in the blind spot region, and outputting audible,tactile or visual feedback, via an output system, to a rider of themotorcycle when the motorcycle is determined to be located in the blindspot region.

In embodiments, the processes include outputting audible feedback to adriver of the one or more other vehicles when the motorcycle isdetermined to be located in the blind spot region.

In embodiments, defining the blind spot region around the one or moreother vehicles uses the detected object data and a relative velocity ofthe one or more other vehicles and the motorcycle.

In embodiments, the detected object data includes a bounding box aroundeach detected motorcycle in the video and an associated labelclassifying the bounding box as a motorcycle. In embodiments, definingthe blind spot region around the one or more other vehicles includesspatially assigning a predetermined blind spot region at a predeterminedlocation relative to the bounding box.

In embodiments, visual feedback is output to the rider via a riderlighting device in the form of a plurality of lights arranged in a ring.Individual lights are lit to indicate directionality of the blind spotregion relative to the motorcycle. In embodiments, the plurality oflights emit different colors depending on immediacy of a threat from themotorcycle being in the blind spot region.

In another aspect, an obstacle detection and notification system for amotorcycle is provided. The system includes a forward looking camera anda backward looking camera mountable to the motorcycle, a cellularconnectivity device, an enhanced map database including a navigation mapand a collision risk map layer of crowd sourced, georeferenced collisiondata, and a Global Positioning System (GPS) device providing a locationof the motorcycle. A processor is in operable communication with the ofthe forward looking camera and the backward looking camera, the cellularconnectivity device, the enhanced map database, and the GPS device. Theprocessor executes program instructions to execute processes including:reporting any accident conditions via the cellular connectivity device,receiving updates to the collision risk map layer via the cellularconnectivity device, determining a risk of a collision based on thelocation of the motorcycle from the GPS device and the collision riskmap layer, receiving video from the forward looking camera and thebackward looking camera, performing a computer vision and machinelearning based object detection and tracking process to detect, classifyand track obstacles in the video and to output detected object data,performing obstacle proximity detection processing using the detectedobject data to provide detected obstacle data, outputting audible,tactile or visual feedback, via an output system, to a rider of themotorcycle based on the detected obstacle data, and adapting theobstacle proximity detection processing and the outputting of audible,tactile or visual feedback based on the determined risk of collision.

In embodiments, the obstacle proximity detection processing is adaptedbased on the determined risk of collision so as to spatially focus theobstacle proximity detection processing on a region of the videocorresponding to a location where the determined risk of collision ishigh.

In embodiments, audible, tactile or visual feedback is output, via anoutput system, to a rider of the motorcycle based on the determined riskof a collision.

In embodiments, the obstacle proximity detection processing is adaptedbased on the determined risk of collision so as to become active orincrease in processing frequency when the determined risk of collisionis relatively high.

In embodiments, the outputting of audible, tactile or visual feedback isadapted so as to visually identify an increased risk of collision whenthe determined risk of collision is relatively high.

In embodiments, reporting any accident conditions via the cellularconnectivity device includes reporting location and time, the collisionrisk map layer includes georeferenced collision data and time referencedcollision data, and the processes include determining the risk of thecollision based on the location of the motorcycle from the GPS device,current time and the collision risk map layer.

In embodiments, the processes include determining an accident conditionbased on a high acceleration event and determining the high accelerationevent based on data from of the GPS device, an inertial measurement unitand computer vision processing of the video.

In another aspect, an obstacle detection and notification system for amotorcycle is provided. The system includes a forward looking thermalcamera and a backward looking thermal camera mountable to the motorcycleand a processor in operable communication with the forward lookingthermal camera and the backward looking thermal camera. The processorexecutes processes including receiving video from the forward lookingthermal camera and the backward looking thermal camera, performing acomputer vision and machine learning based object detection and trackingprocess to detect, classify and track obstacles in the video and tooutput detected object data, performing obstacle proximity detectionprocessing using the detected object data to provide detected obstacledata, and outputting audible, tactile or visual feedback, via an outputsystem, to a rider of the motorcycle based on the detected obstacledata.

In embodiments, a cellular connectivity device is included. Theprocesses include reporting any accident conditions based on data fromof a GPS device, a inertial measurement unit and computer vision processof the video.

In embodiments, performing obstacle proximity detection processingincludes identifying an animal presenting an obstacle in the road.

In embodiments, performing obstacle proximity detection processingincludes identifying an animal presenting an obstacle in the road andreporting an accident condition includes reporting the identified animalpresenting the obstacle in the road.

In embodiments, the processes include receiving crowd sourced,georeferenced and time referenced data concerning identified animalspresenting obstacles in a road and outputting audible, tactile or visualfeedback, via an output system, to a rider of the motorcycle based onthe crowd sourced, georeferenced and time referenced data.

In embodiments, the computer vision and machine learning based objectdetection and tracking process detects and classifies a snake or othernon-human animal in the video and outputs corresponding detected objectdata.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary embodiments will hereinafter be described in conjunctionwith the following drawing figures, wherein like numerals denote likeelements, and wherein:

FIG. 1 is a functional block diagram of a motorcycle that includes anobstacle detection and notification system, in accordance with anexemplary embodiment;

FIG. 2 is a functional block diagram of the obstacle detection andnotification system of FIG. 1, in accordance with an exemplaryembodiment;

FIG. 3 is a rider lighting display device included in the obstacledetection and notification system of FIGS. 1 and 2, in accordance withan exemplary embodiment;

FIG. 4 is a flowchart of a method for implementing obstacle detectionand notification including reverse blind spot detection, which can beused in connection with the motorcycle of FIG. 1 and the obstacledetection and notification system of FIG. 2, in accordance with anexemplary embodiment;

FIG. 5 is a flowchart of a method for implementing obstacle detectionand notification including use of a collision risk heat map, which canbe used in connection with the motorcycle of FIG. 1 and the obstacledetection and notification system of FIG. 2, in accordance with anexemplary embodiment; and

FIG. 6 is a flowchart of a method for implementing obstacle detectionand notification including performing animal detection using thermalimaging, which can be used in connection with the motorcycle of FIG. 1and the obstacle detection and notification system of FIG. 2, inaccordance with an exemplary embodiment.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and isnot intended to limit the application and uses. Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, brief summary or thefollowing detailed description. As used herein, the term module refersto an application specific integrated circuit (ASIC), an electroniccircuit, a processor (shared, dedicated, or group) and memory thatexecutes one or more software or firmware programs, a combinationallogic circuit, and/or other suitable components that provide thedescribed functionality.

FIG. 1 illustrates a motorcycle 100 according to an exemplaryembodiment. As described in greater detail further below, the motorcycle100 includes an obstacle detection and notification system 200 (ODNS)including dual camera machine visioning (MV) with artificial intelligenttechnology (AI) to detect and predict obstacles and provide feedback toa rider of the motorcycle 100.

As depicted in FIG. 1, the motorcycle 100 includes, in addition to theabove-referenced ODNS 200, a body 114 and two wheels 116. The riderwears a helmet 102, which may be communicatively coupled to the ODNS200, as described further below. The body 114 includes an engine (notshown), a braking system (not shown) and handles (not shown) forsteering a front wheel. In one embodiment, the engine comprises acombustion engine. In other embodiments, the engine is an electricmotor/generator, instead of, or in addition to, the combustion engine.Still referring to FIG. 1, the engine is coupled to at least one of thewheels 116 through one or more transmission systems. The braking systemprovides braking for the motorcycle 100. The braking system receivesinputs from the driver via a brake pedal (not depicted) or a brake leverand provides appropriate braking via brake units (also not depicted).The driver also provides inputs via an accelerator handle (not depicted)as to a desired speed or acceleration of the motorcycle 100.

Referring back to the exemplary embodiment of FIG. 1, the motorcycle 100includes one or more cameras 210, 212 as part of a computer visionsystem. The one or more cameras 210, 212 can include a forward-lookingcamera 210 to capture an external scene ahead of the motorcycle 100 anda backward-looking camera 212 to capture an external scene behind themotorcycle 100. The forward-looking camera(s) 210 can be positionedabove a motorcycle headlight, beneath the motorcycle headlight, withinthe motorcycle headlight (e.g. if it is integrated thereto during themanufacturing thereof), or in any other manner that provides theforward-looking camera(s) with a clear view to the area in front of themotorcycle 100. The backward-looking camera(s) 212 can be positionedabove a motorcycle rear light, beneath the motorcycle rear light, withinthe motorcycle rear light (e.g. if it is integrated thereto during themanufacturing thereof), or in any other manner that provides thebackward-looking camera(s) 212 with a clear view to the area in the backof the motorcycle 100. The cameras 210, 212 may be wide angled camerascapable of viewing any angle above 60°, 90°, or even in the range of130° to 175° or more of a forward scene or backward scene. The cameras210, 212 may be monocular cameras and may provide at least RGB (Red,Green, Blue) video (made up of frames of image data). In otherembodiments, the cameras 210, 212 are stereoscopic cameras. In someembodiments herein, the cameras 210, 212 include thermal imaging (orinfrared) capabilities. The forward-looking camera(s) 210 and thebackward-looking camera(s) 212 can have a resolution of at least twoMega-Pixel (MP), and in some embodiments at least five MP. Theforward-looking camera(s) 210 and the backward-looking camera(s) 212 canhave a frame rate of at least twenty Frames-Per-Second (FPS), and insome embodiments at least thirty FPS. Additional cameras may be includedsuch as forward-looking and backward looking narrow angle cameras, whichmay have greater accuracy at larger ranges.

Although FIG. 1 shows the forward-looking camera(s) 210 and thebackward-looking camera(s) 212, the motorcycle can include additionalsensors including forward-looking and/or backward-looking radardevice(s) 214 (as shown in FIG. 2), a plurality of laser range finders,or any other sensor that can support obstacle detection and prediction.

With additional reference to FIG. 2, the ODNS 200 includes a controller204, an output system 206, the forward and backward looking cameras 210,212, the radar device 214, a cellular connectivity device 216, a GPSdevice 218, a local communications device 252 and an enhanced mapdatabase 254, in an exemplary embodiment. The ODNS 200 monitors asurrounding area of the motorcycle 100 for proximity events including atleast one of: vehicles in a rider's blind spots, rider is possibly inother vehicles blind spots (reverse blind spot), common road objects(e.g. approaching an unexpected stopped vehicle, pedestrians, etc.),unusual road objects (e.g. desert animals laying on warm road atnighttime), and fast approaching vehicles from behind. The ODNS 200 maypredict potential collisions (e.g. a vehicle unexpectedly pulling out infront of the motorcycle 100) using georeferenced high-risk motorcyclecollision locations obtained from previous collision data via atelematics feed. Various outputs may be provided to both the rider andexternal vehicles such as activating a rear brake light on themotorcycle when a vehicle is fast approaching the motorcycle 100, forexample. The ODNS 200, in one example, provides these functions inaccordance with the methods 400, 500 and 600 described further below inconnection with FIGS. 4 to 6. The ODNS 200 includes hardware to beinstalled onto the motorcycle and associated software embodied in thecontroller 204 that controls the functions described herein. The ODNS200 may be installed on the motorcycle 100 by technicians as a retrofitor during manufacturing of the motorcycle 100. Some elements of the ODNS200 may be included in a rider's mobile telecommunications device suchas the controller 204 (or part thereof), the rider lighting displaydevice 220 and the video display device 228. The rider light displaydevice 220 would be graphically presented on a display of the mobiletelecommunications device rather than through LEDs as in the integratedhardware system described further herein.

Continuing to refer to FIG. 2, a functional block diagram is providedfor the ODNS 200 of FIG. 1, in accordance with an exemplary embodiment.The controller 204 is coupled to the cameras 210, 212, the radar device214, the cellular connectivity device 216, the GPS device 218, theenhanced map database 254, the local communications device 252 and theoutput system 206. The controller 204 receives video data from thecameras 210, 212 and, based thereon, performs computer vision based andmachine learning based object detection and tracking, reverse blind spotdetection, collision risk prediction, obstacle proximity detection andother operations described further herein. The controller 204 providesrider feedback concerning detected obstacles or potential obstacles, Thecontroller 204 may additionally provide feedback to other vehicles andpotentially also to electronically controlled components of themotorcycle 100 so as to implement, for example, automatic braking,automatic throttle control, automatic gear shifting, etc. The controller204 can be located under a seat of the motorcycle 100, but canalternatively be located in other places in a motorcycle such as behinda display panel between the handles of the motorcycle 100. Thecontroller 204 can be connected to a battery of the motorcycle 100, orit can have its own power supply.

As depicted in FIG. 2, the controller 204 comprises a computer system.In the depicted embodiment, the computer system of the controller 204includes a processor 230, a memory 232, a storage device 236, and a bus238. The processor 230 performs the computation and control functions ofthe controller 204, and may comprise any type of processor or multipleprocessors, single integrated circuits such as a microprocessor, or anysuitable number of integrated circuit devices and/or circuit boardsworking in cooperation to accomplish the functions of a processing unit.During operation, the processor 230 executes one or more programs 240contained within the memory 232 and, as such, controls the generaloperation of the controller 204 and the computer system of thecontroller 204, generally in executing the processes described herein,such as the methods 400, 500, 600 described further below in connectionwith FIGS. 4 to 6. The one or more computer programs 240 include atleast an object detection and tracking module 242, an obstacle proximitydetection module 246, a reverse blind spot detection module 244 and acollision risk prediction module 248 for performing steps of the methods400, 500, 600 described in detail below.

The processor 230 is capable of executing one or more programs (i.e.,running software) to perform various tasks encoded in the program(s),particularly the object detection and tracking module 242, the obstacleproximity detection module 246, the reverse blind spot detection module244 and the collision risk prediction module 248. The processor 230 maybe a microprocessor, microcontroller, application specific integratedcircuit (ASIC) or other suitable device as realized by those skilled inthe art.

The memory 232 can be any type of suitable memory. This would includethe various types of dynamic random access memory (DRAM) such as SDRAM,the various types of static RAM (SRAM), and the various types ofnon-volatile memory (PROM, EPROM, and flash). In certain examples, thememory 232 is located on and/or co-located on the same computer chip asthe processor 230.

The bus(es) 238 serves to transmit programs, data, status and otherinformation or signals between the various components of the computersystem of the controller 204 and between the various hardware componentsincluding the output system 206, forward and backward looking cameras210, 212, the cellular connectivity device 216, the GPS device 218, thelocal communications device 252 and the enhanced map database 254. Thebus(es) 238 can be any suitable physical or logical means of connectingcomputer systems and components. This includes, but is not limited to,direct hard-wired connections, fiber optics, infrared and wireless bustechnologies.

The storage device 236 can be any suitable type of storage apparatus,including direct access storage devices such as hard disk drives, flashsystems, floppy disk drives and optical disk drives. In one exemplaryembodiment, the storage device 236 comprises a program product fromwhich memory 232 can receive a program 240 (including computer modules242, 244, 246, 248) that executes one or more embodiments of one or moreprocesses of the present disclosure, such as the steps of the methods400, 500 and 600 (and any sub-processes thereof). In another exemplaryembodiment, the program product may be directly stored in and/orotherwise accessed by the memory 232 and/or a disk (e.g., disk), such asthat referenced below. The enhanced map database 254 may be stored onthe memory 232.

It will be appreciated that while this exemplary embodiment is describedin the context of a fully functioning computer system, those skilled inthe art will recognize that the mechanisms of the present disclosure arecapable of being distributed as a program product with one or more typesof non-transitory computer-readable signal bearing media used to storethe program and the instructions thereof and carry out the distributionthereof, such as a non-transitory computer readable medium bearing theprogram and containing computer instructions stored therein for causinga computer processor (such as the processor 230) to perform and executethe program. Such a program product may take a variety of forms, and thepresent disclosure applies equally regardless of the particular type ofcomputer-readable signal bearing media used to carry out thedistribution. Examples of signal bearing media include: recordable mediasuch as floppy disks, hard drives, memory cards and optical disks, andtransmission media such as digital and analog communication links. Itwill similarly be appreciated that the computer system of the controller204 may also otherwise differ from the embodiment depicted in FIG. 2,for example in that the computer system of the controller 204 may becoupled to or may otherwise utilize one or more remote computer systemsand/or other control systems.

In the exemplary embodiment of FIG. 2, the output system 206 includes atleast one of: a rider lighting display device 220, an external lightingsystem 222, a rider speaker 224, a tactile feedback device 226, a videodisplay device 228 and an external speaker 256. The output system 206 isresponsive to output data from the controller 204 to provide visual,audible or tactile feedback to a rider of the motorcycle 100 or to adriver of an external vehicle. Upon identification of a threat to themotorcycle 100 by the controller 204, the controller 204 commands analert to a rider of the motorcycle 100 in order to enable the rider toperform measures to eliminate or reduce any risk. The alerts can beprovided in any manner that can be sensed by a rider of the motorcycle100. In some cases, the alert can be visual provided via the riderlighting display device 220, tactile via the tactile feedback device 226and/or audible via the rider speaker 224. Some parts of the outputsystem 206 may be included in the helmet 102 such as the rider speaker224, the tactile feedback device 226 and/or the rider lighting displaydevice 220. The local communications device 252 allows the controller204 to send data to the helmet 102 through any suitable localcommunications protocol such as Bluetooth or WiFi. In one embodiment,the local communications device 252 is facilitated through a localcommunications capability of a rider's mobile telecommunications device.

The rider lighting display device 220 of one example embodiment is shownin FIG. 3. The rider lighting display device 220 includes a plurality oflight emitters 302 that can be activated to indicate presence anddirectionality of an obstacle or a potential obstacle. The riderlighting display device 220 includes a plurality of light emitters 302arranged in a ring around a motorcycle orientation reference graphic304. In the exemplary embodiment, there are eight light emitters 302evenly distributed in the ring shape to provide eight degrees ofdirectionality to the alert. However, more or less light emitters 302could be provided. For example, four light emitters 304 could beincluded to provide four degrees of alert directionality includingforward, backward, left and right. The light emitters 302 may be LEDs inone embodiment. The controller 204 may output data indicatingdirectionality and severity of a collision threat and command the riderlighting display device 220 accordingly. In such an embodiment, thelight emitters 302 can be controlled to emit different colors dependingon the threat level such as red for server alert, orange for high alert,yellow elevated alert and green for low alert or some subset of two orthree of these alert levels. Other arrangements of light emitters 302that allow directionality and threat level severity are possible such aslight strips arranged on mirrors of the motorcycle 100. The riderlighting display device 220 may also be controlled to differentiate atype of threat such as having different flashing frequencies fordifferent threat types. The rider lighting display device 222 could belocated in a display panel between the handles of the motorcycle 100 orcould be projected onto or displayed by a shield of the helmet 102.

The external lighting system 222 includes existing or additional lightsof the motorcycle 100 to indicate to drivers of external vehicles thatthey are a collision threat to the motorcycle when such a determinationis made by the controller 204. For example, the external lighting system222 provides notifications to drivers of other vehicles through a rearbrake light (such as an LED) on the motorcycle 100 when, for example,the controller 204 determines that a vehicle is fast approaching themotorcycle from behind. In this instance, the motorcycle rider may get anotification via the rider lighting display device 220 and the driver ofthe vehicle receives a notification via the rear brake light or otherrear light of the external lighting system 222. Front and/or sideexternal lights could also be included as additional lights or as partof the existing lights of the motorcycle 100 to alert external driversin front of the motorcycle 100 and to the sides of the motorcycle 100.The external speaker 256, which may be an integrated horn of themotorcycle 100 or an additional device, may additionally oralternatively provide a warning to external vehicles or humans of acollision threat with the motorcycle when such a collision threat hasbeen determined by the controller 204. More than one external speaker256 could be arranged around the motorcycle 100 to allow fordirectionality in the audible warning such as front, rear, left side andright side external speakers 256.

In embodiments, the warning notification can be a vibration provided tothe rider of the motorcycle 100 via one or more vibrating or othertactile elements included in the tactile feedback device 226 causingvibration felt by the rider of the motorcycle 100. In some cases, thevibration can be adjusted in accordance with the threat severity, sothat the higher the risk, the stronger the vibration. The vibrationelements may additionally or alternatively be integrated into a jacketworn by the rider of the motorcycle 100, into the seat of the motorcycle100, or into a helmet 102 worn by the rider of the motorcycle 100.

In embodiments, the alert is provided through the rider speaker 224. Thealert is provided as a sound notification to the rider of the motorcycle100 via the one or more rider speakers 224. The rider speakers 224 canbe integrated into the helmet 102 of the rider, or any other speakersthat generate sounds that can be heard by the rider of the motorcycle100. In some cases, the sound notification can be a natural languagevoice notification, providing information of the specific threat typeand/or severity identified and/or the direction of the threat. In somecases, the volume can be adjusted in accordance with the risk severity,so that the higher the risk, the higher the volume.

In embodiments, the video display device 228 provides a live video feedfrom the forward looking camera 210 and/or the backward looking camera212. The live video feed may provide a focused area of the total videodata based on a direction of the threat. The live video feed may besupplemented with graphical indications of any collision threatsdistinguishing different types of threats, different threat levels andthe direction of the threat as determined by the controller 204. In someembodiments, video from the forward and backward looking cameras 210,212 is recorded in the storage device 236.

The various possible output options of the output system 206 describedabove may be provided alone or in any combination. Having described theoutput system 206 and some example audible, visual or tactile feedbackmechanisms to threat severity and directions determined by thecontroller 204, a more detailed description of the software operationsof the controller will be provided.

Continuing to refer to the exemplary embodiment of FIG. 2, the objectdetection and tracking module 242 can be implemented through a number ofobject detection and tracking algorithms. In one embodiment, the objectdetection and tracking module 242 receives video data from the forwardand backward looking cameras 210, 212 and runs the video data, or aderivative thereof, through a machine learning algorithm to classify andlocalize obstacles that the machine learning algorithm is trained todetect. The machine learning algorithm may include a ConvolutionalNeural Network (CNN) or other neural network. One example suitablemachine learning algorithm is You Only Look Once (YOLO). The objectdetection part of the object detection and tracking module 242 providesdetected object data including bounding box size, location andclassification information. Various obstacle classifications arepossible including vehicle, pedestrian, cyclist, non-human animal, etc.The object tracking part of the object detection and tracking module 242tracks a detected object over time (plural frames of video data) inorder to derive velocity and acceleration information for trackedobjects and to predict the obstacle's path. In one embodiment, anextended Kalman filter using a motion model for the tracked object canbe included in the object tracking part. The object detection andtracking module 242 uses intrinsic and extrinsic camera parameters andpossibly also motion parameters from an Inertial Measurement Unit (notshown) or other motion sensors of the motorcycle 100 to provide detectedobject data in real world coordinates in a coordinate frame relative tothe motorcycle 100. The object detection and tracking module 242 is thusable to output location, velocity, acceleration, path projection andclassification data for each detected object in forward and backwardlooking scenes. This data is included in detected object data providedto the obstacle proximity detection module 246 and the reverse blindspot detection module 244. The object detection and tracking module 242has been described at a relatively high level for the purposes ofconciseness. It should be appreciated that a number of object detectionand tracking applications are available in the literature that receivevideo data and use computer vision and machine learning processing toclassify and track detected objects.

The obstacle proximity detection module 246 receives the detected objectdata and data on the motion of the motorcycle 100 from the IMU or fromother motion sensors such as a wheel speed sensor. In this way, theobstacle proximity detection module 246 is able to project the path ofthe motorcycle 100 and the projected paths of moving obstacles todetermine whether there is any collision threat or any potential spatialoverlap with detected stationary obstacles. A collision threat may bedetermined by a projected collision occurring in less than a firstpredetermined time threshold. In some embodiments, a plurality ofdifferent time thresholds may be used so as to define different threatlevels. Furthermore, the mutual motion projections between themotorcycle 100 and the various moving obstacles can be compared todetermine a directionality of the threat by determining a directionrelative to the motorcycle 100 that an obstacle is travelling. Forstationary obstacles, the directionality can be determined based on arelative location between the motorcycle 100 and the detected locationof the stationary object. The obstacle proximity and detection module246 can output collision threat data that is indicative of collisionthreat severity level and directionality, which can be included inoutput data for output system 206 to activate various output devices asdescribed above. The obstacle proximity and detection module 246 mayadditionally distinguish detected threat types in the output data.

The obstacle proximity detection module 246 detects a plurality of kindsof proximity events including vehicles in a rider's blind spots, a rideris possibly in other vehicles blind spots (reverse blind spot) asdescribed further below, common road objects (e.g. approaching anunexpected stopped vehicle, pedestrians, etc.), unusual road objects(e.g. desert animals laying on warm road at nighttime) as describedfurther herein, fast approaching vehicles from behind, potentialcollisions (e.g. a vehicle may unexpectedly pull out in front of therider but has not yet) as described further herein, etc.

In some embodiments, the object detection and tracking module 242 istrained to detect non-human animals based on thermal imaging receivedfrom the forward-looking camera 210, the backward looking camera 212 orother forward or backward looking camera particularly suited to thermalimaging. Such an embodiment is designed to detect non-human animals inlow visibility conditions such as fog and nighttime. The obstacleproximity detection module 246 receives the detected non-human animaldata and responsively outputs a collision threat based on time topotential collision with the non-human animal, which will determine athreat level, and a direction of the threat. The output system 206responsively outputs an indication of threat level and directionalityand optionally also type of threat (e.g. via a specific color orsequence of light emitters 302 or a particular sound or annunciationfrom the rider speaker 224). In one embodiment, the non-human animal isa snake. Snakes and other desert animals can present a particular dangerto motorcycles because they often lay on warm roads at nighttime.

In embodiments, the reverse blind spot detection module 244 receives thedetected object data from the object detection and tracking module 244.The reverse blind spot detection module 244 determines one or more blindspot regions for one or more detected vehicles. A blind spot in avehicle is an area around the vehicle that cannot be directly observedby the driver while at the controls. A blind spot may occur behind theside window at a location that is also not visible in the side viewmirrors. Motorcycles are narrower than cars and are more liable to beingwholly located within a vehicle's blind spot. The reverse blind spotdetection module 244 may retrieve a predetermined blind spot region andconnect it to a detected vehicle at a location where a blind spot wouldbe. The predetermined blind spot region may be in image coordinates andscaled and rotated in image space according to a distance andorientation between the vehicle and the motorcycle 100, which can beperformed based on camera intrinsic and extrinsic parameters.Alternatively, the predetermined blind spot region is provided in realworld coordinates and the detected vehicles are transformed into realworld space so that the predetermined blind spot regions can beconnected thereto. In some embodiments, the object detection andtracking module 244 is trained to detect vehicles and additionally sideview mirrors on the vehicles, which can additionally support accuratelocation of the blind spot region attached based on a bounding boxaround the side view mirrors. Alternatively, an average positionrelative to a bounding box around the vehicle could be used to connectthe blind spot region. In some embodiments, the predetermined blind spotregion is enlarged based on a relative speed between the other vehicleand the motorcycle 100. The relative speed is known from the detectedobject data output from the object detection and tracking module 242 asdescribed above. In this way, the faster the closing speed, the morelikely that a reverse blind spot detection is made to reflect that thedriver of the other vehicle would have less time to check the sidemirrors and spot the motorcycle 100.

The reverse blind spot detection module 244 may compare a location ofthe motorcycle 100 with the blind spot region(s) to determine whetherthe motorcycle is located within the blind spot region of any othervehicle. The location of the motorcycle 100 can be obtained by the GPSdevice 218 or from localization based on computer vision processing ofthe video data from the forward and backward looking cameras 210, 212.In another embodiment, the reverse blind spot detection module 244 cancompare a path trajectory of the motorcycle 100, which can be determinedbased on a motion model for the motorcycle 100, and location,acceleration and velocity data for the motorcycle 100. The location,acceleration and velocity data for the motorcycle is obtained from GPSdevice 218 and/or motion sensors of the motorcycle 100. Based on whetherthe location of the motorcycle 100 is within a blind spot region or isprojected to be within the blind sport region within a predeterminedamount of time, the reverse blind spot detection module 244 providesoutput data indicating a reverse blind spot threat and a directionalitythereof. The reverse blind spot detection module 244 may additionallyoutput a threat level based on the proximity of the vehicle and themotorcycle in its blind sport region or based on their relative speeds.The output system 206 outputs visual, tactile or audible feedback, whichidentifies direction, severity and optionally also type of threat asdescribed above.

In embodiments, the collision risk prediction module 248 predictslocations and times when there is an added risk of collision with anobstacle using crowd data regarding motorcycle, and possibly other,vehicle accidents or near accidents included in a collision risk maplayer 270 of the enhanced map database 254. The collision risk map layer270 is a map layer that is regularly updated with accident or nearaccident information through the cellular connectivity device 216. Thecellular connectivity device 216 can be a 4G or 5G data communicationsdevice, for example. In some embodiments, the cellular connectivitydevice 216 is facilitated through a rider's mobile telecommunicationsdevice. The collision risk map layer 270 provides georeferenced and timereferenced crowd data on where collisions or near collisions haveoccurred. In one embodiment, the collision risk map layer 270distinguishes between motorcycle accidents or near accidents and thoseof other vehicles since the threats to a motorcycle can be different tothose to other types of vehicle and the collision risk prediction module248 operates on the motorcycle specific data. In one example, thecollision risk map layer 270 may reflect a greater probability ofcollisions at a bar driveway location and time of day (e.g. after happyhour). The collision risk prediction module 248 filters the collisionrisk map layer 270 using the current time and current location of themotorcycle 100, which is known from the location data provided by theGPS device 218 so as to determine upcoming relatively high risklocations (e.g. locations where the collision risk is predicted to begreater than a threshold at the current time). The collision riskprediction module 248 may provide output data to the output system 206indicating the directionality, a threat level and optionally a type ofthreat.

The collision risk prediction module 248 may provide the output data tothe obstacle proximity detection module 246 and/or the object detectionand tracking module 242. The obstacle proximity detection module 246and/or the object detection and tracking module 242 is responsive to thecollision risk indication and location in the output data to increase afrequency of, or activate, the object detection and obstacle proximitydetection processes and/or to spatially focus the object detection andobstacle proximity processes based on the location of the collisionrisk. In an additional or alternative embodiment, the obstacle proximitydetection module 246 can increase in sensitivity in response to thecollision risk data from the collision risk prediction module so as toindicate a higher threat level or to lower proximity thresholds so as tomore readily provide output data to the output system 206 describing acollision threat. As such, the ODNS is placed on high alert and isspecifically focused when the collision risk map layer 270 predicts anupcoming (e.g. within set a range of the forward looking and backwardlooking cameras 210, 212) collision risk.

In embodiments, the controller 204 determines accident conditions basedon high acceleration information being obtained from the IMU (not shown)or the GPS device 218. The controller 204 can ascertain from theacceleration information whether the motorcycle has been subjected to animpact or an emergency stop. The controller 204 reports such accidentconditions along with location, time and optionally date information,which can be obtained from the GPS device 218, to a remote map server(not shown) through the cellular connectivity device 216. A crowd ofmotorcycles will similarly report accident conditions, allowing theremote map server to create a continually updating collision risk maplayer 270 that is periodically pushed to the motorcycle 100 via thecellular connectivity device 216.

In one embodiment, the location and time of non-human animals (e.g.snakes on a road) detected by thermal imaging are reported to the remotemap server as an accident condition for motorcycles. The collision riskmap layer 270 can integrate crowd sourced times and locations fornon-human animals being on the road. This information couldalternatively be included in a different map layer. The collision riskprediction module 248 can factor in high risk of non-human animals beingon the road at certain locations and at certain times of the day andprovide output data representing the collision risk to the output system206 and the object detection and tracking module 242 and the obstacleproximity detection module 246. In this way, object detection andtracking processing can be activated or increased in frequency whenthere is a collision risk above a predetermined threshold according tothe non-human animal data in the collision risk map layer. The outputsystem 206 can provide an audible, tactile or visual alert concerningthe collision risk, which can identify directionality and optionallyalso type of non-human animal (e.g. a snake graphic). The obstacleproximity detection module 246 may increase in sensitivity (as describedabove) when there is a high risk of collision with a non-human animal onthe road.

In embodiments, the ODNS 200 is in operable communication with anapplication on a rider's mobile telecommunications device. The rider'smobile telecommunications device with the designed application may beused for configuration of the various motorcycle module settings (e.g.warning thresholds, preferred alert methods, etc.). The applicationcould also be used for pulling stored video from the cameras (actionvideo, etc.) 210, 212 or recorded video from the storage device 236.

FIG. 4 is a flowchart of a method 400 for reverse blind spot detection,in accordance with one embodiment. The method 400 can be implemented inconnection with the motorcycle 100 of FIG. 1 and the ODNS 200 of FIG. 2,in accordance with an exemplary embodiment. The method 400 may beimplemented continuously during motorcycle operation or be invoked orincreased in regularity based on collision risk data from the collisionrisk prediction module 248.

As depicted in FIG. 4, the method 400 includes the step 410 of receivingvideo data from the forward looking camera 210 and/or the backwardlooking camera. Object detection and tracking is performed on the videodata to classify and localize other vehicles in step 430. In step 430, ablind spot region is defined around each detected vehicle or eachdetected vehicle within a certain range of the motorcycle 100. The blindspot regions may be changed in size based on a relative speed betweenthe motorcycle 100 and the other vehicle so as to be enlarged, thegreater the relative speed. In step 440, a determination is made whetherthe motorcycle 100 is located in a blind spot region or is projected tobe located in a blind spot region within a predetermined amount of time.When a determination has been made of a reverse blind spot threat,audible, visual or tactile feedback is provided to the rider of themotorcycle 100 warning of the reverse blind spot threat, the directionof the threat and optionally also identifying the type of threat.

FIG. 5 is a flowchart of a method 500 for collision risk determination,in accordance with one embodiment. The method 500 can be implemented inconnection with the motorcycle 100 of FIG. 1 and the ODNS 200 of FIG. 2,in accordance with an exemplary embodiment. The method 500 may beimplemented continuously during motorcycle operation.

In step 510, a collision risk is determined from collision risk data ina collision risk map layer 270 of the enhanced map database 254. Thecollision risk data is time referenced and georeferenced accident ornear accident data reported from a crowd of motorcycles. Based oncurrent time and current location of the motorcycle 100, any collisionrisk information is extracted from the collusion risk map layer 270 andassessed for relevance based on the collision risk being above apredetermined threshold. An indication of the upcoming collision riskand the direction of the collision risk is output for further processingin step 560.

In step 520, video data is received from the forward looking camera 210and/or the backward looking camera 212. In step 530, object detectionand tracking is performed based on the video data to classify andlocalize objects in the captured scene. In step 540, obstacle proximitydetection is performed using the detected object data from step 530including detecting vehicles in rider's blind spots, detecting commonroad objects (e.g. approaching an unexpected stopped vehicle,pedestrians, etc.), and detecting unusual road objects (e.g. desertanimals laying on warm road at nighttime). The obstacle proximitydetection step 540 further determines directionality and immediacy ofany proximity threat and outputs corresponding collision threat data. Instep 550, audible, visual and/or tactile feedback is provided to therider through the output system 206 based on the collision threat datato indicate to the rider the direction of the collision threat, theexistence of the collision threat and optionally also the type ofcollision threat.

In step 560, the object detection and tracking step 530 is adaptedand/or the obstacle proximity detection step 560 is adapted and/or therider feedback step 550 is adapted when collision risk data from step510 is determined based on the collision risk map layer 270. That is,steps 530 and 540 may be adapted so as to become active from a dormantstate or steps 530 and 540 may be increased in frequency. Additionally,or alternatively, steps 530 and 540 may be adapted to spatially focus ona region of the video data in which a high collision risk has beendetermined. Additionally, or alternatively, the sensitivity of step 540may be heightened by changing proximity thresholds used in detectingcollision threats so as to more readily output an indication of aproximal collision threat. Additionally, the output system 206 may issuean audible, tactile and/or visual alert regarding a potential collisionrisk identified in step 510 including the direction and optionally alsothe type of collision risk (e.g. increased traffic from a bar at thistime causing accident conditions, snakes on the road at this time,etc.).

FIG. 6 is a flowchart of a method 600 for detecting non-human animals ona road, in accordance with one embodiment. The method 600 can beimplemented in connection with the motorcycle 100 of FIG. 1 and the ODNS200 of FIG. 2, in accordance with an exemplary embodiment. The method600 may be implemented continuously during motorcycle operation or maybecome active or increased in frequency based on the collision risk datafrom the collision risk prediction module indicating potential fornon-human animals on the road in the vicinity of the motorcycle 100.

In step 610, video data is received from a forward looking thermalcamera and/or a backward looking thermal camera. In step 620, objectdetection and tracking are performed to localize and classify objectsincluding non-human animals that will show up vividly in the thermalimaging. The object detection and tracking processes may be trained tolabel non-human animals as a category or to specify one or more types ofnon-human animal such as snakes. In step 630, obstacle proximitydetection is performed to detect proximal collision threats includingcollision threats with detected non-human animals. In step 640, thedetected non-human animals are reported to a remote map server throughthe cellular connectivity device 216. In step 650, a map updateincluding crowd detected non-human animals is received. The map updateincludes time and location for each detection. The map update isincluded in the enhanced map database 254 possibly as part of thecollision risk map layer. In this way, the method 500 described abovewith respect to FIG. 5 takes into account georeferenced and timereferenced non-human animals on the road when determining the collisionrisk data. In step 660, audible, tactile and/or visual rider feedback isprovided to the non-human rider when step 630 determines a proximalcollision threat because of a detected non-human animal on the road. Therider feedback can indicate direction, severity (e.g. immediacy) andoptionally the type of threat (e.g. distinguishing a non-human animalfrom other types of threat or specifying the detected type of non-humananimal such as a snake).

It will be appreciated that the disclosed methods, systems, andmotorcycles may vary from those depicted in the Figures and describedherein. For example, the motorcycle 100 and the ODNS 200 and/or variouscomponents thereof may vary from that depicted in FIGS. 1 and 2 anddescribed in connection therewith. In addition, it will be appreciatedthat certain steps of the method 400 may vary from those depicted inFIGS. 4, 5 and 6 and/or described above in connection therewith. It willsimilarly be appreciated that certain steps of the method describedabove may occur simultaneously or in a different order than thatdepicted in FIGS. 4, 5 and 6.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or exemplary embodiments are only examples, and arenot intended to limit the scope, applicability, or configuration of thedisclosure in any way. Rather, the foregoing detailed description willprovide those skilled in the art with a convenient road map forimplementing the exemplary embodiment or exemplary embodiments. Itshould be understood that various changes can be made in the functionand arrangement of elements without departing from the scope of thedisclosure as set forth in the appended claims and the legal equivalentsthereof.

1. (canceled)
 2. (canceled)
 3. (canceled)
 4. (canceled)
 5. (canceled) 6.(canceled)
 7. (canceled)
 8. An obstacle detection and notificationsystem for a motorcycle, the system comprising: at least one of aforward looking camera and a backward looking camera mountable to themotorcycle; a cellular connectivity device; an enhanced map databaseincluding a navigation map and a collision risk map layer of crowdsourced, georeferenced collision data; a Global Positioning System (GPS)device providing a location of the motorcycle; and at least oneprocessor in operable communication with the at least one of the forwardlooking camera and the backward looking camera, the cellularconnectivity device, the enhanced map database, and the GPS device, theat least one processor configured to execute program instructions,wherein the program instructions are configured to cause the at leastone processor to execute processes including: reporting any accidentconditions via the cellular connectivity device; receiving updates tothe collision risk map layer via the cellular connectivity device;determining a risk of a collision based on the location of themotorcycle from the GPS device and the collision risk map layer;receiving video from the at least one of the forward looking camera andthe backward looking camera; performing a computer vision and machinelearning based object detection and tracking process to detect, classifyand track obstacles in the video and to output detected object data;performing obstacle proximity detection processing using the detectedobject data to provide detected obstacle data; and outputting audible,tactile or visual feedback, via an output system, to a rider of themotorcycle based on the detected obstacle data, wherein: at least one ofthe object detection and tracking process and the obstacle proximitydetection processing is adapted based on the determined risk ofcollision, wherein the object detection and tracking process and/or theobstacle proximity detection processing is adapted based on thedetermined risk of collision so as to be performed at an increasedprocessing frequency at locations where the determined risk of collisionis determined to be greater than a threshold.
 9. An obstacle detectionand notification system for a motorcycle, the system comprising: atleast one of a forward looking camera and a backward looking cameramountable to the motorcycle; a cellular connectivity device; an enhancedmap database including a navigation map and a collision risk map layerof crowd sourced, georeferenced collision data; a Global PositioningSystem (GPS) device providing a location of the motorcycle; and at leastone processor in operable communication with the at least one of theforward looking camera and the backward looking camera, the cellularconnectivity device, the enhanced map database, and the GPS device, theat least one processor configured to execute program instructions,wherein the program instructions are configured to cause the at leastone processor to execute processes including: reporting any accidentconditions via the cellular connectivity device; receiving updates tothe collision risk map layer via the cellular connectivity device;determining a risk of a collision based on the location of themotorcycle from the GPS device and the collision risk map layer;receiving video from the at least one of the forward looking camera andthe backward looking camera; performing a computer vision and machinelearning based object detection and tracking process to detect, classifyand track obstacles in the video and to output detected object data;performing obstacle proximity detection processing using the detectedobject data to provide detected obstacle data; and outputting audible,tactile or visual feedback, via an output system, to a rider of themotorcycle based on the detected obstacle data, wherein: at least one ofthe object detection and tracking process and the obstacle proximitydetection processing is adapted based on the determined risk ofcollision, wherein the object detection and tracking process and/or theobstacle proximity detection processing is adapted based on thedetermined risk of collision so as to spatially focus the objectdetection and tracking process and/or the obstacle proximity detectionprocessing on a region of the video corresponding to a location wherethe determined risk of collision is determined to be greater than athreshold.
 10. The obstacle detection and notification system of claim8, comprising outputting audible, tactile or visual feedback, via anoutput system, to a rider of the motorcycle based on the determined riskof a collision.
 11. The obstacle detection and notification system ofclaim 9, wherein the object detection and tracking process and/or theobstacle proximity detection processing is adapted based on thedetermined risk of collision so as to become active or increase inprocessing frequency when the determined risk of collision is relativelyhigh.
 12. The obstacle detection and notification system of claim 8,wherein the outputting of audible, tactile or visual feedback is adaptedso as to visually identify an increased risk of collision when thedetermined risk of collision is relatively high.
 13. The obstacledetection and notification system of claim 8, wherein: reporting anyaccident conditions via the cellular connectivity device includesreporting location and time; the collision risk map layer includesgeoreferenced collision data and time referenced collision data; anddetermining the risk of the collision is based on the location of themotorcycle from the GPS device, current time and the collision risk maplayer.
 14. The obstacle detection and notification system of claim 8,wherein the program instructions are configured to cause the at leastone processor to execute processes including determining an accidentcondition based on a high acceleration event and determining the highacceleration event based on data from at least one of the GPS device, aninertial measurement unit and computer vision processing of the video.15. (canceled)
 16. (canceled)
 17. The obstacle detection andnotification system of claim 9, wherein performing obstacle proximitydetection processing includes identifying a non-human animal presentingan obstacle in the road.
 18. The obstacle detection and notificationsystem of claim 9, wherein performing obstacle proximity detectionprocessing includes identifying a non-human animal presenting anobstacle in the road and wherein reporting any accident conditionsincludes reporting the identified non-human animal presenting theobstacle in the road.
 19. The obstacle detection and notification systemof claim 17, wherein the program instructions are configured to causethe at least one processor to execute processes including receivingcrowd sourced, georeferenced and time referenced data concerningidentified non-human animals presenting obstacles in a road andoutputting audible, tactile or visual feedback, via an output system, toa rider of the motorcycle based on the crowd sourced, georeferenced andtime referenced data.
 20. The obstacle detection and notification systemof claim 9, wherein performing the computer vision and machine learningbased object detection and tracking process detects and classifies asnake on the road in the video and outputs corresponding detected objectdata.
 21. An method of obstacle detection and notification system for amotorcycle, the method comprising: at least one of a forward lookingcamera and a backward looking camera mountable to the motorcycle; acellular connectivity device; an enhanced map database including anavigation map and a collision risk map layer of crowd sourced,georeferenced collision data; a Global Positioning System (GPS) deviceproviding a location of the motorcycle; and at least one processor inoperable communication with the at least one of the forward lookingcamera and the backward looking camera, the cellular connectivitydevice, the enhanced map database, and the GPS device, the at least oneprocessor configured to execute program instructions, wherein theprogram instructions are configured to cause the at least one processorto execute processes including: reporting, via at least one processorassociated with the motorcycle, any accident conditions via a cellularconnectivity device associated with the motorcycle; receiving, via theat least one processor, updates to a collision risk map layer of crowdsourced, georeferenced collision data via the cellular connectivitydevice, the collision risk map layer included in an enhanced mapdatabase associated with the motorcycle, the collision risk map layerincluding a navigation map and the collision risk map layer;determining, via the at least one processor, a risk of a collision basedon a location of the motorcycle and the collision risk map layer, thelocation of the motorcycle obtained from a GPS device associated withthe motorcycle; receiving, via the at least one processor, video from atleast one of a forward looking camera and a backward looking cameraassociated with the motorcycle; performing, via the at least oneprocessor, a computer vision and machine learning based object detectionand tracking process to detect, classify and track obstacles in thevideo and to output detected object data; performing, via the at leastone processor, obstacle proximity detection processing using thedetected object data to provide detected obstacle data; and outputtingaudible, tactile or visual feedback, via an output system, to a rider ofthe motorcycle based on the detected obstacle data, wherein: at leastone of the object detection and tracking process and the obstacleproximity detection processing is adapted based on the determined riskof collision, wherein the object detection and tracking process and/orthe obstacle proximity detection processing is adapted based on thedetermined risk of collision so as to be performed at an increasedprocessing frequency at locations where the determined risk of collisionis determined to be greater than a threshold.
 22. The method of claim21, comprising outputting audible, tactile or visual feedback, via theoutput system, to a rider of the motorcycle based on the determined riskof a collision.
 23. The method of claim 21, wherein the outputting ofaudible, tactile or visual feedback is adapted so as to visuallyidentify an increased risk of collision when the determined risk ofcollision is relatively high.
 24. The method of claim 21, wherein:reporting any accident conditions via the cellular connectivity deviceincludes reporting location and time; the collision risk map layerincludes georeferenced collision data and time referenced collisiondata; and determining the risk of the collision is based on the locationof the motorcycle from the GPS device, current time and the collisionrisk map layer.
 25. The method of claim 21, comprising determining, viathe at least one processor, an accident condition based on a highacceleration event and determining the high acceleration event based ondata from at least one of the GPS device, an inertial measurement unitand computer vision processing of the video.
 26. The method of claim 21,wherein performing obstacle proximity detection processing includesidentifying a non-human animal presenting an obstacle in the road. 27.The method of claim 21, wherein performing obstacle proximity detectionprocessing includes identifying a non-human animal presenting anobstacle in the road and wherein reporting any accident conditionsincludes reporting the identified non-human animal presenting theobstacle in the road.